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Embedded carbon emissions indicator - EV02033

Description

The aim of the proposed project work is to develop a time series of input-output tables for the UK by using an automated data optimisation procedure that allows the construction of national input-output and environmental databases in greatest possible sector disaggregation that can be used for a multi-region environmental input-output model in the future. Thus the work will set the basis for a UK++ MRIO model , enabling future multi-regional analyses of environmental impacts associated with UK trade flows, including the provision of a robust indicator for embedded emissions.
In order to achieve this aim initial data estimates need to be made, data constraints need to be defined and specific optimisation algorithms need to be developed and implemented.
In order to derive reliable and robust estimates for embedded emissions, it is important to explicitly consider the production efficiency and emissions intensity of a number of trading countries and world regions in an international trade model, which is globally closed and sectorally deeply disaggregated.
The implementation and application of a full multi-regional input-output framework poses three basic challenges: data availability, data reconciliation and computability. These issues and possible practical solutions are discussed in detail in Wiedmann et al. (2006a). In the following we focus on the important issue of data handling in a MRIO model.
Compiling the required data, estimating missing data and balancing conflicting data in the right way is the most crucial part of an MRIO framework. Most resources should be devoted to this part of the work as a good handling of data ensures consistency, robustness and repeatability of the whole approach. The data system should allow to
· include data in different classifications,
· aggregate or disaggregate sectors, depending on the research question,
· find a compromise solution for conflicting data,
· cope with suppressed data,
· estimate missing data,
· accommodate different years for the analysis of time series.